Sampling feature \(X_S\)  creates a new distribution \((\tilde{X}_{S}, X_C)\) , with a (possibly) different variance for a given point across models.
If the variance of \(\hat{f}\)  changes for \(\tilde{X}_S\) , this leads to a bias in the PFI estimate.
Besides this bias due to the extrapolation variance, the assumption of model unbiasedness is critical or even unreasonable for regions outside of \({P}_{XY}\) , since there is no feedback whether the model matches the DGP in these regions.
Furthermore, the DGP might have a probability density of zero for regions of extrapolation.
This means that the marginal PFI for dependent features can have a conceptual problem, as the permutation might create data points that are in conflict with the DGP [1]}, [2]}.Imagine a person with a weight of 4kg and a height of 2m.
